87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import GridSearchCV, cross_val_score
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from .common import Benchmark, Estimator, Predictor
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from .datasets import _synth_classification_dataset
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from .utils import make_gen_classif_scorers
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class CrossValidationBenchmark(Benchmark):
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"""
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Benchmarks for Cross Validation.
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"""
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timeout = 20000
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param_names = ['n_jobs']
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params = (Benchmark.n_jobs_vals,)
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def setup(self, *params):
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n_jobs, = params
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data = _synth_classification_dataset(n_samples=50000, n_features=100)
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self.X, self.X_val, self.y, self.y_val = data
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self.clf = RandomForestClassifier(n_estimators=50,
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max_depth=10,
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random_state=0)
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cv = 16 if Benchmark.data_size == 'large' else 4
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self.cv_params = {'n_jobs': n_jobs,
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'cv': cv}
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def time_crossval(self, *args):
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cross_val_score(self.clf, self.X, self.y, **self.cv_params)
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def peakmem_crossval(self, *args):
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cross_val_score(self.clf, self.X, self.y, **self.cv_params)
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def track_crossval(self, *args):
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return float(cross_val_score(self.clf, self.X,
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self.y, **self.cv_params).mean())
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class GridSearchBenchmark(Predictor, Estimator, Benchmark):
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"""
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Benchmarks for GridSearch.
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"""
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timeout = 20000
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param_names = ['n_jobs']
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params = (Benchmark.n_jobs_vals,)
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def setup_cache(self):
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super().setup_cache()
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def make_data(self, params):
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data = _synth_classification_dataset(n_samples=10000, n_features=100)
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return data
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def make_estimator(self, params):
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n_jobs, = params
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clf = RandomForestClassifier(random_state=0)
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if Benchmark.data_size == 'large':
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n_estimators_list = [10, 25, 50, 100, 500]
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max_depth_list = [5, 10, None]
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max_features_list = [0.1, 0.4, 0.8, 1.0]
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else:
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n_estimators_list = [10, 25, 50]
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max_depth_list = [5, 10]
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max_features_list = [0.1, 0.4, 0.8]
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param_grid = {'n_estimators': n_estimators_list,
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'max_depth': max_depth_list,
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'max_features': max_features_list}
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estimator = GridSearchCV(clf, param_grid, n_jobs=n_jobs, cv=4)
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return estimator
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def make_scorers(self):
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make_gen_classif_scorers(self)
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